**2.8. Definition of the equipment procurement processes (EPP)**

**2.5. Design of the I4 value scorecard (I4-VS)**

64 New Trends in Industrial Automation

organization should define its own specific I4-VS scorecard.

**2.6. Design of the I4 equipment scorecard (I4-ES)**

the gap between the two domains.

*motion & data synchronization* [7].

**2.7. Definition of the equipment types**

Even though this work identified the requirement for an I4-VS scorecard this work did not focus on its creation. The definition of value for a Business is a specialized domain well outside the scope of this research. Generic tools such as the McKinsey Digital Compass which maps the Industry 4.0 levers to the key value drivers [6] are freely available. Each company's definition of value will almost certainly be quite different and confidential thus it very unlikely that it will be possible to provide a generic I4-VS scorecard. Based on this assumption, each

On first impressions it appeared that the VDMA's *Toolbox Industry 4.0 for Product* [5] is not relevant to this study because this study is not focused on the product which is being manufactured. The production equipment is the Original Equipment Manufacturer's (OEM) Product, so it is relevant. By assigning the same numeric score to each column it becomes possible to create a second numeric scorecard namely I4-ES Scorecard for Equipment. This approach also facilitates the utilization of *Current*, and *Target* scores to enable the quantification and management of improvement. Unfortunately, the common denominators of not *connected, partially connected, fully connected, analyze & predict* and *adaptive* which are on the I4-PS do not map directly the I4-ES. Even though it is tempting to change some of the content of the VDMA Toolboxes this has been resisted to ensure that adherence to the VDMA's best working practice is always retained. The minor embellishments proposed in these sections only assist with the usability of the tool but do not compromise the integrity of the content. It is important to note that the I4-PS and I4-ES are owned by the Business and Technical Stakeholder's respectively. This provides a very similar format of scorecard which will undoubtedly assist in bridging

An in-depth review of leading assembly and packaging equipment Original Equipment Manufacturers (OEMs) revealed a common denominator; *They all transport and perform actions on the product* [7]. A review of the logic and motion technology providers also revealed a common denominator; *they all recommend the utilization of servo motors with decentralized drives synchronized via motion control networks as opposed to mechanical synchronization* [7]. But not all equipment types require motion synchronization between the transport system and stations organized around the transport system. It is perfectly valid to have equipment which operates in an asynchronous or semi-synchronous fashion. At the other extreme, there is a growing requirement to synchronize process data between the stations and transport system. Using the technologies which synchronize the transport and stations as a separator this work defined and published a novel classification method for the different Equipment Types whereby *0—No motion synchronization, 1—Mechanical motion synchronization, 2—Mechanical & electronic motion synchronization, 3—Software based motion synchronization and 4—Software based*  With an Industry 3.0 EPP, the mechanical discipline typically drives the process. The Information Technology (IT) and Information Systems (IS) infrastructure are not installed, simulated or tested at the OEM's premises, thus it is not possible to test many of the critical functions at Functional Acceptance Test (FAT). This results in an undesirable situation whereby many equipment defects only become apparent after the equipment is in production. Such defects are extremely expensive, and sometimes impossible, to rectify when the equipment is in production, where limited OEM support is available. These defects undoubtedly have a significant negative impact on OEE and regulatory compliance during production. This EPP is undesirable for any equipment type but it totally unsuitable for Type 3 and Type 4 and thus cannot be utilized for Industry 4.0 Equipment.

Industry 4.0 has enabled significant advances in Industrial IT, Internet based collaborative technologies and cloud computing. These advances have all but eliminated the historical infrastructural constraints which I3-EPPs were exposed to, because it is now technically possible to simulate virtually any IT or IS, in the form of an I4 Infrastructure, at the OEM's site. The provision of an Industry 4.0 infrastructure for the FAT does not, in isolation, address all the issues which have been identified during this research. The unacceptable level of software defects which exist in *custom software* [8, 9] justifies the utilization of an *Integrated Software Quality Tool* [10], which focuses on requirement risk, test and defect management during the construction of the equipment. By adding Information Technology Infrastructure Library (ITIL®) into the scope of Integrated Software Quality, a Service Desk can be provided which facilitates the efficient provision of incident, problem and change processes to manage Data-Information-Knowledge-Wisdom (DIKW) [12]. The inclusion of these tools in a novel fashion enables the creation of an I4-EPP, as outlined in **Figure 3**. This is significantly more holistic than the I3-EPP and enables the creation of a collaborative supply network "*In collaborative supply networks, OEMs will be able to offer value-added services (e.g. maintenance, upgrade) or even sell their 'products as a service'. Remote service management helps to improve* 

**Figure 3.** The Industry 4.0 equipment procurement process (I4-EPP).

*equipment uptime, reduce costs for servicing (e.g. travel costs), increase service efficiency (e.g. firstvisit-fix-rates) and accelerate innovation processes (e.g. remote update of device software)*" [11].

research is not suggesting that such a simplistic formula is capable of accurately representing every situation and it will undoubtedly require future refinement. But in its current format it leverages the lessons learned from OEE and is sufficient to provide a quantifiable benchmark

Industry 3.0 to Industry 4.0: Exploring the Transition http://dx.doi.org/10.5772/intechopen.80347 67

A hierarchical structure and an executable application (The OSE Calculator) were developed to support the DIVOM process. The OSE Rating is at the top of the hierarchy. It is composed of five metrics (D, I, V, O and M) each with three components consisting of 10 Attributes. Attributes are composed of a variable number of requirements which are omitted from The OSE Calculator application to minimize the cognitive load on participants. It is important to note that omitting the requirements significantly increase the dependence on the facilitator. The 10-Attribute scale is organized in order of achievement with 00 being worst to 10 being best in class. This approach has been utilized to expedite user comprehension of the measurement process. The clarity of the Attributes is further augmented by adoption of the standard color coding convention of green = low risk, orange = medium risk, red = high

metric which is fit for purpose.

**Figure 4.** Design of the OSE calculator.

risk and displaying the rating graphically (see **Figure 4**).
